Diabetes Technology Meeting 2022.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 3 to November 5, 2022. Meeting topics included (1) the measurement of glucose, insulin, and ketones; (2) virtual diabetes care; (3) metrics for managing diabetes and predicting outcomes; (4) integration of continuous glucose monitor data into the electronic health record; (5) regulation of diabetes technology; (6) digital health to nudge behavior; (7) estimating carbohydrates; (8) fully automated insulin delivery systems; (9) hypoglycemia; (10) novel insulins; (11) insulin delivery; (12) on-body sensors; (13) continuous glucose monitoring; (14) diabetic foot ulcers; (15) the environmental impact of diabetes technology; and (16) spinal cord stimulation for painful diabetic neuropathy. A live demonstration of a device that can allow for the recycling of used insulin pens was also presented.

How Frequently and for How Long Do Adults With Type 2 Diabetes Use Management Apps? The REALL Study.

OBJECTIVE: The objective of the study is to identify predictors of utilization of a type 2 diabetes (T2D) management App over time for insulin users (IUs) and noninsulin users (NIUs). RESEARCH DESIGN AND METHODS: We followed over 16 weeks a national sample of unselected T2D adults who independently elected to download and pair a CONTOUR DIABETES App with their CONTOUR NEXT ONE glucose meter. App use and frequency of glucose testing were recorded. Baseline surveys recorded participant demographic, disease status, distress, medication taking, and views of technology to predict utilization. RESULTS: Mean age was 51.6 years (108 IUs; 353 NIUs), 48% were female, time with diabetes was 6.9 years, and self-reported HbA1c was 8.1% (36.3 mmol/mol). Mean duration of App use was 85.4 days and 40% stopped using the App before 16 weeks. Continuous users were older and reported higher distress, better medication taking, and more positive attitudes toward technology (all P < .01). IUs tested more frequently than NIUs, but frequency and intensity of testing decreased markedly for both groups over time. More predictors of App use frequency and testing occurred for NIUs than IUs: older age, higher HbA1c, lower distress, more medication taking (all P < .05). CONCLUSIONS: App use and testing decreased markedly over time. Variations in the predictors of frequency of App use suggest that the utilization of mobile technologies requires a tailored approach that addresses the specific needs of individual users, compared with adopting a one-size-fits-all strategy, and that IUs and NIUs may require very different strategies of customization.

Impact of Blood Glucose Monitoring System Accuracy on Clinical Decision Making for Diabetes Management.

BACKGROUND: The accuracy of blood glucose monitoring systems (BGMS) is crucial for the safe and effective management of diabetes mellitus. Despite standardization of accuracy assessment procedures and requirements, various studies have shown that the accuracy of BGMS on the market can vary considerably. This article therefore provides health care professionals and users with an intuitive illustration of the impact of BGMS accuracy on clinical decision making. MATERIAL AND METHODS: Several hypothetical patient scenarios based on blood glucose (BG) levels in the low, normal, and high BG range are devised. Using data from a recent BGMS accuracy study, a method for calculating the expected range of BG readings from four examined BGMS at the selected BG levels is introduced. Based on these ranges, it is illustrated how clinical decisions and subsequent outcomes of the hypothetical patients are affected by the expected inaccuracies of the BGMS. RESULTS: The range of expected BGMS readings for the same true BG level can vary considerably between different BGMS. The discussion of hypothetical patient scenarios revealed that the use of some BGMS could be associated with an increased risk of adverse events such as failure to detect hypoglycemia, driving with an unsafe BG level, delay of treatment intervention in diabetes during pregnancy, or the failure to prevent diabetic ketoacidosis. CONCLUSIONS: This article can support both health care professionals and patients to understand the impact of BGMS accuracy in a relatable, clinical context. Furthermore, it is suggested that current accuracy requirements might be insufficient for the prevention of adverse clinical outcomes in certain circumstances.

Hospital Diabetes Meeting 2022.

The annual Virtual Hospital Diabetes Meeting was hosted by Diabetes Technology Society on April 1 and April 2, 2022. This meeting brought together experts in diabetes technology to discuss various new developments in the field of managing diabetes in hospitalized patients. Meeting topics included (1) digital health and the hospital, (2) blood glucose targets, (3) software for inpatient diabetes, (4) surgery, (5) transitions, (6) coronavirus disease and diabetes in the hospital, (7) drugs for diabetes, (8) continuous glucose monitoring, (9) quality improvement, (10) diabetes care and educatinon, and (11) uniting people, process, and technology to achieve optimal glycemic management. This meeting covered new technology that will enable better care of people with diabetes if they are hospitalized.

Can A Basic Management App Paired With A Glucose Meter Help Reduce Glucose Levels Among Adults With Type 2 Diabetes? The REALL Study.

OBJECTIVE: Evaluations of technology to help adults manage type 2 diabetes (T2D) have yielded mixed results. We analyzed the effectiveness of a free app linked to a glucose meter to study reductions in glucose levels over time among a self-selected sample of adults with T2D. RESEARCH DESIGN AND METHODS: Adults with T2D >12 months, >21 years, ability to read English (insulin using-IU and non-insulin using-NIU) who independently elected to pair their CONTOUR NEXT ONE meter with the CONTOUR DIABETES App were invited to participate. Glucose data from baseline to 16 weeks were uploaded to the cloud (N = 461). Assessment of diabetes distress, medication taking, quality of life, and hypoglycemia concerns occurred at baseline, six, and 16 weeks. RESULTS: Findings indicated a significant decrease in weekly glucose levels over time: baseline mean = 169 (62.0) (9.4 mmol/L; 3.44); 16-week mean = 146.5 (36.0) (8.1 mmol/L; 2.0) (P < .001), with no IU and NIU differences. Largest reductions occurred during the first six weeks, with no later rebound effects. Significant, though modest, improvements in global quality of life (P = .03), hypoglycemia concerns (P = .01), and diabetes distress (P < .001) occurred over 16 weeks. CONCLUSIONS: Making an App for monitoring glucose easily available for download with a glucose meter can be helpful for self-selected adults with T2D. Effective utilization assumes that users are sufficiently motivated and engaged, are comfortable and trusting of the technology, and have sufficient knowledge of how to make use of the glucose data.

Choice of Continuous Glucose Monitoring Systems May Affect Metrics: Clinically Relevant Differences in Times in Ranges.

BACKGROUND: Continuous glucose monitoring-derived parameters are becoming increasingly important in the treatment of people with diabetes. The aim of this study was to assess whether these parameters, as calculated from different continuous glucose monitoring systems worn in parallel, are comparable. In addition, clinical relevance of differences was investigated. METHODS: A total of 24 subjects wore a FreeStyle Libre (A) and a Dexcom G5 (B) sensor in parallel for 7 days. Mean glucose, coefficient of variation, glucose management indicator and time spent in different glucose ranges were calculated for each system. Pairwise differences between the two different continuous glucose monitoring systems were computed for these metrics. RESULTS: On average, the two CGM systems indicated an identical time in range (67.9±10.2 vs. 67.9±11.5%) and a similar coefficient of variation; both categorized as unstable (38.1±5.9 vs. 36.0±4.8%). In contrast, the mean time spent below and above range, as well as the individual times spent below, in and above range differed substantially. System A indicated about twice the time spent below range than system B (7.7±7.2 vs. 3.8±2.7%, p=0.003). This could have led to different therapy recommendations in approximately half of the subjects. DISCUSSION: The differences in metrics found between the two continuous glucose monitoring systems may result in different therapy recommendations. In order to make adequate clinical decisions, measurement performance of CGM systems should be standardized and all available information, including the HbA1c, should be utilized.

Variation of Mean Absolute Relative Differences of Continuous Glucose Monitoring Systems Throughout the Day.

BACKGROUND: There is an increasing use of continuous glucose monitoring (CGM) by people with diabetes. Measurement performance is often characterized by the mean absolute relative difference (MARD). However, MARD is influenced by a number of factors and little is known about whether MARD is stable throughout the day. MATERIAL AND METHODS: A total of 24 participants with type 1 diabetes were enrolled in the study. The study was performed for seven in-patient days. Participants wore two CGM systems in parallel and performed additional frequent blood glucose (BG) measurements. On two days, glucose excursions were induced.MARD was calculated between pairs of CGM and BG values, with BG values serving as reference values. ARD values calculated from CGM-BG pairs were grouped by hour of the day. Results were analyzed separately for glucose excursion days and for regular days. RESULTS: Total MARDs for the complete study duration were 12.5% ± 3.6% and 13.2% ± 2.4% (n = 24). Throughout the day marked variability of MARD was observed (8.0% ± 1.3%-16.3% ± 2.9% (G5); 9.1% ± 1.4%-16.3% ± 5.3% (FL), up to n = 157 each). Low(est) MARD values were observed before breakfast and dinner, when subjects were in or near a fasting state. Especially after breakfast and lunch, MARD values were higher than average. CONCLUSIONS: Analytical performance of the two CGM systems, assessed by MARD, was found to vary markedly throughout the day. Activities of daily life likely triggered these variations. An increasing number of CGM users base therapeutic decisions on CGM values, and they should be aware of these variations of performance throughout the day.

Self-measurement of Blood Glucose and Continuous Glucose Monitoring - Is There Only One Future?

Monitoring glycaemic control in patients with diabetes has evolved dramatically over the past decades. The introduction of easy-to-use systems for self-monitoring of blood glucose (SMBG) utilising capillary blood samples has resulted in the availability of a wide range of systems, providing different measurement quality. Systems for continuous glucose monitoring (CGM) - used mainly in patients with type 1 diabetes (T1D) - were made possible by the development of glucose sensors that measure glucose levels in the interstitial fluid (ISF) in the subcutaneous tissue of the skin. CGM readings might not correspond exactly to SMBG measurement results taken at the same time, especially during rapid changes in either blood glucose or ISF glucose levels. The mean absolute relative difference is the most popular method used for characterising the measurement performance of CGM systems. Unlike the International Organization for Standardization 15197:2013 criteria for SMBG systems, no accuracy standards for CGM systems exist. Measurement quality of CGM systems can vary based on several factors, limiting their safety and effective use in managing diabetes. Patients have to be trained adequately to make safe and efficient use of CGM systems (like with SMBG systems). Also, systems for CGM must be evaluated in terms of patient safety and the ability to provide accurate measurements regardless of the fluctuation of glucose levels. As new technological advancements in glucose monitoring are essential for improved management options of diabetes, such as automated insulin dosing systems, there is a need for a critical view of all such developments. It is likely that both, SMBG and CGM systems, will play important future roles in the treatment of diabetes.

Improved treatment satisfaction in patients with type 1 diabetes treated with insulin glargine 100U/mL versus neutral protamine Hagedorn insulin: An exploration of key predictors from two randomized controlled trials.

AIM: Investigate contributors to treatment satisfaction in type 1 diabetes (T1D). METHODS: Post-hoc analysis using the Diabetes Treatment Satisfaction Questionnaire status version (DTSQs) in 771 T1D patients from two 28-week trials comparing once-daily insulin glargine 100U/mL (Gla-100) with once- or twice-daily NPH neutral protamine Hagedorn (NPH) insulin. RESULTS: Gla-100 was associated with a significant improvement in treatment satisfaction versus NPH (overall population adjusted mean [standard error] DTSQs change from baseline: +1.13 [0.30] versus -0.04 [0.31]; p=0.006). In the overall population, treatment satisfaction improvement with all insulin regimens was related to less frequent severe hypoglycemia (coefficient-0.077; p=0.040) and HbA1c reduction (-0.066; p=0.082). By treatment regimen, relationships between treatment satisfaction and these outcomes approached or attained statistical significance for NPH insulin, but not Gla-100. In the overall population, predictors of treatment satisfaction improvement included: Gla-100 treatment (estimate 1.17, p=0.006), lower baseline DTSQs (-0.57, p<0.001), study (-1.01, p=0.019), lower severe hypoglycemia rate (0.17, p=0.012), and higher baseline HbA1c (0.44, p=0.014). By treatment regimen, these predictors remained significant for NPH insulin. CONCLUSIONS: Gla-100 resulted in a significant improvement in treatment satisfaction versus NPH insulin, independent of baseline disease characteristics and clinical outcomes.

BeAM value: an indicator of the need to initiate and intensify prandial therapy in patients with type 2 diabetes mellitus receiving basal insulin.

INTRODUCTION: In patients with type 2 diabetes mellitus (T2DM) with uncontrolled glycemia despite ongoing upward titration of basal insulin, targeting postprandial hyperglycemia may be required. Nevertheless, the point at which basal insulin is fully optimized and postprandial glucose (PPG) should be targeted with additional treatment remains unclear. We report here on the BeAM value (difference between bedtime and morning blood glucose values) as an indicator of the need to target PPG. METHODS: This study had 3 stages: exploratory, main, and proof-of-concept analyses. For the exploratory and main analyses, data were pooled from phase 3 trials in adults with T2DM adding basal insulin to oral antidiabetic drugs (OADs). The main analysis included only patients who did not reach A1C ≤7.0% (53 mmol/mol) at week 24. The proof-of-concept analysis used pooled data from phase 3 trials in adults with T2DM adding insulin glargine and a single insulin glulisine injection to OADs. RESULTS: In patients undergoing basal insulin titration, BeAM value increased over 24 weeks (27.8-61.7 mg/dL, n=1188; 32.6-71.2 mg/dL, n=553; exploratory and main analyses, respectively). There were significant correlations between week 24 BeAM value and postprandial contribution to hyperglycemia (Pearson's correlation coefficient (r)=0.375, p<0.001; r=0.396, p<0.001; exploratory and main analyses, respectively). When PPG was targeted (proof-of-concept analysis), the BeAM value reduced from 77.0 to 40.4 mg/dL (n=299). CONCLUSIONS: The BeAM value described in this study is a simple, easy-to-calculate value that may identify patients with T2DM using basal insulin that need targeting of postprandial control rather than advancing basal insulin dose.

Weight change in patients with type 2 diabetes starting basal insulin therapy: correlates and impact on outcomes.

BACKGROUND: An increase in body weight is a commonly perceived effect of insulin therapy for type 2 diabetes mellitus, and this may serve as a barrier to insulin initiation and usage. OBJECTIVE: To investigate the baseline clinical and demographic factors associated with weight gain during insulin glargine therapy, and the implications of weight change on clinical outcomes. METHODS: This was a retrospective analysis of patient-level data from phase 3 or 4 randomized controlled, treat-to-target (fasting plasma glucose [FPG] ≤ 100 mg/dL) trials evaluating basal insulin glargine for ≥ 24 weeks. The Pearson correlation coefficient and Cochran-Armitage trend statistic were used to calculate the existence of a trend between absolute and relative weight change, and relative glycated hemoglobin (HbA1c) change from baseline; likelihood of achieving target HbA1c < 7.0%; change from baseline FPG; insulin dose requirements; incidence of hypoglycemia; and adverse events. RESULTS: Eleven studies were included, encompassing a total of 2140 patients. Patients starting insulin glargine treatment gained a mean ± standard deviation 1.8 ± 3.7 kg (4.0 ± 8.2 lb). Most patients had limited weight change (± 2.5 kg or 5.5 lb). Younger age, higher baseline HbA1c, and higher baseline FPG were predictive of greater weight gain (P < 0.0001). Those who gained more weight experienced the largest decrease from baseline in HbA1c and FPG. More weight gain was associated with higher insulin dose requirements, an increased risk of experiencing either symptomatic or glucose-confirmed (< 70 mg/dL) hypoglycemia, and more adverse events. Older patients (> 65 years) were less likely to gain weight or to experience glucose-confirmed hypoglycemia, but more likely to experience severe hypoglycemia. CONCLUSIONS: In this retrospective analysis of patient-level data, most patients had a stable weight (defined as ± 2.5 kg) after 24 weeks of insulin glargine, and weight gain varied with patient demographics. Therefore, insulin glargine can be used in these patient groups with type 2 diabetes without expectation of significant weight gain.